Book Image

R Data Mining

Book Image

R Data Mining

Overview of this book

R is widely used to leverage data mining techniques across many different industries, including finance, medicine, scientific research, and more. This book will empower you to produce and present impressive analyses from data, by selecting and implementing the appropriate data mining techniques in R. It will let you gain these powerful skills while immersing in a one of a kind data mining crime case, where you will be requested to help resolving a real fraud case affecting a commercial company, by the mean of both basic and advanced data mining techniques. While moving along the plot of the story you will effectively learn and practice on real data the various R packages commonly employed for this kind of tasks. You will also get the chance of apply some of the most popular and effective data mining models and algos, from the basic multiple linear regression to the most advanced Support Vector Machines. Unlike other data mining learning instruments, this book will effectively expose you the theory behind these models, their relevant assumptions and when they can be applied to the data you are facing. By the end of the book you will hold a new and powerful toolbox of instruments, exactly knowing when and how to employ each of them to solve your data mining problems and get the most out of your data. Finally, to let you maximize the exposure to the concepts described and the learning process, the book comes packed with a reproducible bundle of commented R scripts and a practical set of data mining models cheat sheets.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
14
Epilogue

Support vector machines


 

It is now time to move on to support vector machines, to see if they help us better define the profile of those customers more inclined to get into default status.

First of all, you should notice that support vector machines are way more recent models since they where developed around the 1990s. Secondly, you should notice that when we talk about SVMs, we are actually talking about a family of models rather than a single one.

I am going to show you now just what you need to know to understand the main concepts behind this model, but I will point out to you some good references in case you want to deepen your knowledge of the topic.

The intuition behind support vector machines

There are three main concepts you have to bear in mind when talking about support vector machines:

  • The hyperplane
  • The maximal margin classifier
  • The support vector

The hyperplane

The hyperplane can be considered as the first brick to be employed when building a support vector machine.

Have you ever played...